In order to improve the quality of financial images, this paper proposes a financial image quality enhancement method based on deep learning and graph neural networks. Using nonlinear functions to compensate for lighting in financial images, converting them from RGB to SHV images with color tone, saturation, and brightness channels, achieving color space transformation in financial images. Utilizing deep learning techniques to repair brightness residuals in financial images, and utilizing graph neural networks for super-resolution processing to enhance image texture features, achieving quality enhancement of financial images based on deep learning and graph neural networks. The experimental results show that the application of the design method effectively improves the structural similarity and peak signal-to-noise ratio of financial images, making it suitable for enhancing the quality of financial images.